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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction
课程评论：没有评论
课程名称：序列、时间序列与预测 课程概述：如果你是一名软件开发人员，希望构建可扩展的人工智能算法，那么了解如何使用工具来构建这些算法是非常必要的。本专业课程将教授你在使用流行的开源机器学习框架TensorFlow时的最佳实践。在这第四门课程中，你将学习如何在TensorFlow中构建时间序列模型。首先，你将实现最佳实践来准备时间序列数据。接着，你将探索如何使用递归神经网络（RNN）和一维卷积网络（1D ConvNets）进行预测。最后，你将结合在整个专业课程中所学的知识，使用真实数据构建太阳黑子预测模型。 Andrew Ng的机器学习课程和深度学习专项课程教授了机器学习和深度学习的最重要和基础的原则。此新的deeplearning.ai TensorFlow专业课程将教你如何利用TensorFlow实现这些原则，从而开始构建和应用可扩展的模型来解决现实问题。为了深入理解神经网络的工作原理，建议你参加深度学习专项课程。 课程大纲： 1. **序列与预测**：这一章节关注在处理顺序时间序列数据时的一些独特考虑因素，比如特定日期的温度或网站访问者数量的变化。我们将讨论预测这些时间序列未来值的各种方法。 2. **时间序列的深度神经网络**：在探讨了时间序列的一些常见属性（如趋势和季节性）以及使用统计方法进行预测后，我们将开始教授神经网络如何识别和预测时间序列。 3. **时间序列的递归神经网络**：递归神经网络及其长短期记忆网络非常适合分类和预测顺序数据。本周我们将探索如何使用它们来处理时间序列。 4. **真实世界的时间序列数据**：在深度神经网络和递归神经网络的基础上，我们将加入卷积操作，并将所有内容整合在一起，使用一个测量数百年太阳黑子活动的真实数据系列，看看能否进行预测。
Name:Sequences and Prediction
Description:Hi Learners and welcome to this course on sequences and prediction! In this course we'll take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series, building on what you've learned in previous courses!
Name:Deep Neural Networks for Time Series
Description:Having explored time series and some of the common attributes of time series such as trend and seasonality, and then having used statistical methods for projection, let's now begin to teach neural networks to recognize and predict on time series!
Name:Recurrent Neural Networks for Time Series
Description:Recurrent Neural networks and Long Short Term Memory networks are really useful to classify and predict on sequential data. This week we'll explore using them with time series...
Name:Real-world time series data
Description:On top of DNNs and RNNs, let's also add convolutions, and then put it all together using a real-world data series -- one which measures sunspot activity over hundreds of years, and see if we can predict using it.
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
